Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean

YANG Guang; HE Hailun; WANG Yuan; HAN Xiqiu; WANG Yejian

doi:10.1007/s13131-016-0847-4

利用西北印度洋船测数据评估基于卫星的海表面温度

doi: 10.1007/s13131-016-0847-4

1.
浙江大学海洋学院, 舟山, 316021;卫星海洋环境动力学国家重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012
2.
卫星海洋环境动力学国家重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012
3.
海底科学重点实验室, 国家海洋局第二海洋研究所, 杭州, 310012

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出版历程
- 收稿日期: 2015-10-26
- 修回日期: 2016-01-27

Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean

1.
Ocean College, Zhejiang University, Zhoushan 316021, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
2.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
3.
Key Laboratory of Submarine Geosciences, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China

摘要

摘要: 本文描述了一次西北印度洋夏季进行的船载气象观测。我们使用船载数据评估卫星海表面温度，并发现影响海表面温度误差的主要因素。本文考虑两种卫星数据，一种是微波遥感产品——热带降雨测量任务微波成像仪TMI数据，另一种是融合了微波、红外线和漂流浮标数据的融合数据——业务化海表面温度和海冰分析OSTIA数据。结果表明，融合数据的日平均海表面温度平均误差和均方根误差都小于微波遥感数据。这一结果证实了融合红外线和漂流浮标数据以提高微波遥感海表面温度数据质量的必要性。进一步，本文分析海表面温度误差对于气象参数的依赖性，这些气象参数包括风速、气温、相对湿度、气压和能见度。结果表明，风速与TMI海表面温度误差的相关系数最大；气温是影响OSTIA海表面温度误差最重要的因素，同时，相对湿度与OSTIA海表面温度误差的相关系数也较高。
- 船载观测 /
- 海表面温度 /
- 西北印度洋 /
- 热带降雨测量任务微波成像仪 /
- 业务化海表面温度和海冰分析
Abstract: A summer-time shipboard meteorological survey is described in the Northwest Indian Ocean. Shipboard observations are used to evaluate a satellite-based sea surface temperature (SST), and then find the main factors that are highly correlated with errors. Two satellite data, the first is remote sensing product of a microwave, which is a Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the second is merged data from the microwave and infrared satellite as well as drifter observations, which is Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). The results reveal that the daily mean SST of merged data has much lower bias and root mean square error as compared with that from microwave products. Therefore the results support the necessary of the merging infrared and drifter SST with a microwave satellite for improving the quality of the SST. Furthermore, the correlation coefficient between an SST error and meteorological parameters, which include a wind speed, an air temperature, a relative humidity, an air pressure, and a visibility. The results show that the wind speed has the largest correlation coefficient with the TMI SST error. However, the air temperature is the most important factor to the OSTIA SST error. Meanwhile, the relative humidity shows the high correlation with the SST error for the OSTIA product.
- shipboard survey /
- sea surface temperature /
- Northwest Indian Ocean /
- Tropical Rainfall Measuring Mission Microwave Imager /
- Operational Sea Surface Temperature and Sea Ice Analysis

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参考文献(30)

Bell M M, Montgomery M T, Emanuel K A. 2012. Air-sea enthalpy and momentum exchange at major hurricane wind speeds observed during CBLAST. J Atmos Sci, 69:3197-3222

Burkill P H, Mantoura R F C, Owens N J P. 1993. Biogeochemical cycling in the northwestern Indian Ocean:a brief overview. Deep-Sea Res 40(3):643-649

Chen Dake, Lian Tao, Fu Congbin, et al. 2015. Strong influence of westerly wind bursts on El Niño diversity. Nat Geosci, 8:339-345

Dong C M, Nencioli F, Liu Y, et al. 2011. An automated approach to detect oceanic eddies from satellite remotely sensed sea surface temperature data. IEEE Geoscience and Remote Sensing Letters, 8(6):1055-1059

Donlon C J, Casey K S, Robinson I S, et al. 2009. The GODAE high-resolution sea surface temperature pilot project. Oceanography, 22:34-45

Donlon C J, Martin M, Stark J, et al. 2012. The operational sea surface temperature and sea ice analysis (OSTIA) system. Remote Sensing of Environment, 116:140-158

Donlon C J, Minnett P J, Gentemann C, et al. 2002. Toward improved validation of satellite sea surface skin temperature measurements for climate research. J Climate, 15:353-369

Gentemann C L. 2014. Three way validation of MODIS and AMSR-E sea surface temperatures. J Geophys Res, 119:2583-2598

Gentemann C L, Wentz F J, Mears C A, et al. 2004. in situ validation of tropical rainfall measuring mission microwave sea surface temperatures. Journal of Geophysical Research, 109:C04021

Hu Haibo, Hong Xiaoyuan, Zhang Yuan, et al. 2013. The critical role of Indian summer monsoon on the remote forcing between Indian and Northwest Pacific during El Niño decaying year. Sci China:Earth Sci, 56(3):408-417

Lian Tao, Chen Dake, Tang Youmin, et al. 2014. Effects of westerly wind bursts on El Niño:a new perspective. Geophys Res Lett, 41(10):3522-3527

Liu Xiaohui, Chen Dake, Dong Changming, et al. 2016. Variation of the Kuroshio intrusion pathways northeast of Taiwan using the Lagrangian method. Sci China:Earth Sci, 59:268-280

Liu Xiaohui, Dong Changming, Chen Dake, et al. 2014. The pattern and variability of winter Kuroshio intrusion northeast of Taiwan. J Geophys Res, 119:5380-5394

O'Carroll A G, August T, Le Borgne P, et al. 2012. The accuracy of SST retrievals from METOP-A IASI and AVHRR using the EUMETSAT OSI-SAF matchup dataset. Remote Sensing of Environment, 126:184-194

O'Carroll A G, Eyre J R, Saunders R W. 2008. Three-way error analysis between AATSR, AMSR-E, and in situ sea surface temperature observations. J Atmos Oceanic Technol, 25(7):1197-1207

Parekh A, Sharma R, Sarkar A. 2007. A comparative assessment of surface wind speed and sea surface temperature over the Indian Ocean by TMI, MSMR, and ERA-40. J Atmos Oceanic Technol, 24:1131-1142

Park K A, Chung J Y, Kim K, et al. 1994. A study on comparison of satellite-tracked drifter temperature with satellite-derived sea surface temperature of NOAA/NESDIS. Korean J Remote Sens, 10(2):83-107

Park K A, Chung J Y, Kim K, et al. 1999. Sea surface temperature retrievals optimized to the East Sea (Sea of Japan) using NOAA/AVHRR data. Marine Technology Society Journal, 33(1):23-35

Park K A, Lee E Y, Chung S R, et al. 2011. Accuracy assessment of sea surface temperature from NOAA/AVHRR data in the seas around Korea and error characteristics. Korean J Remote Sens, 27(6):663-675

Qiu C H, Wang D X, Kawamura H, et al. 2009. Validation of AVHRR and TMI-derived sea surface temperature in the northern South China Sea. Cont Shelf Res, 29(20):2358-2366

Reynolds R W. 1993. Impact of mount Pinatubo aerosols on satellitederived sea surface temperatures. J Climate, 6:768-774

Reynolds R W, Gentemann C L, Corlett G K. 2010. Evaluation of AATSR and TMI satellite SST data. J Climate, 23(1):152-165

Senan R, Anith S, Sengupta D. 2001. Validation of SST and WS from TRMM Using North Indian Ocean Moored Buoy Observations. CAOS Report 2001AS1, Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, Indian

Stark J D, Donlon C J, Martin M J, et al. 2007. OSTIA:an operational, high resolution, real time, global sea surface temperature analysis system. In OCEANS 2007. Marine challenges:coastline to deep sea. Aberdeen:IEEE Ocean Engineering Society, 331-334, doi:10.1109/OCEANSE. 2007. 4302251

Udaya B T V S, Rahman S H, Pavan I D, et al. 2009. Comparison of AMSR-E and TMI sea surface temperature with Argo near-surface temperature over the Indian Ocean. Int J Remote Sens, 30:2669-2684

Walton C C. 1988. Nonlinear multichannel algorithms for estimating sea surface temperature with AVHRR satellite data. J Appl Meteor, 27:115-124

Wentz F J, Gentemann C, Smith D, et al. 2000. Satellite measurements of sea surface temperature through clouds. Science, 288(5467):847-850

Williams G N, Zaidman P C, Glembocki N G, et al. 2014. Comparison between remotely-sensed sea-surface temperature (AVHRR) and in situ records in San Matías Gulf (Patagonia, Argentina). Latin American Journal of Aquatic Research, 42(1):192-203

Wu Qiaoyan, Yan Ying, Chen Dake. 2013. A linear Markov model for East Asian monsoon seasonal forecast. Journal of Climate, 26:5183-5195

Wu Qiaoyan, Yan Ying, Chen Dake. 2016. Seasonal prediction of East Asian monsoon precipitation:skill sensitivity to various climate variabilities. Int J Climatol, 36:324-333

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文章访问数: 1119
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利用西北印度洋船测数据评估基于卫星的海表面温度

doi: 10.1007/s13131-016-0847-4

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Evaluating a satellite-based sea surface temperature by shipboard survey in the Northwest Indian Ocean

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